Hands-on Introduction to Machine Learning for Engineers | AIChE

Hands-on Introduction to Machine Learning for Engineers

If you’re ready to dive into machine learning and apply it to real engineering problems, this course is for you. 

A course tailored to your specific needs as an engineer   

In four days, you’ll gain an intuitive understanding of machine learning and how to implement state-of-the-art methods using Python. Packed with interactive training modules, case studies and learning exercises, this course teaches machine learning in a way any engineer can understand and apply. You’ll learn both theory and practical engineering applications. You’ll become familiar with a wide range of relevant areas, including supervised and unsupervised learning methods, classification and regression. And, you’ll be challenged to apply what you learn to your own problems.

Hands-on learning brings the content to life  

You can choose to learn machine learning concepts using the TCLab device to produce data. Available for purchase, it’s a valuable tool for exploring machine learning concepts using a physical device to produce data.

What if obtaining the hardware is not practical or a USB connection to the computer is not available? Then you can choose the TCLab digital twin—a software-based replica of the TCLab that does not require assessing the physical TCLab device. The digital twin simulates the behavior and responses. In addition, you can interact with and manipulate it to improve learning and allow experimentation.

  • Visualize data to understand relationships and assess data quality
  • Apply linear algebra, statistics, and optimization techniques to create machine learning algorithms
  • Plan applications using engineering and business objectives
  • Assess data information content and predictive capability
  • Detect overfitting and implement strategies to improve prediction
  • Define the differences between classification, regression, and clustering and when to apply each
  • Implement machine learning techniques successfully to complete a group project

Intermediate level with some programming experience. It is ideal if they have already been through Data-Driven Engineering with Python.

Times listed are Eastern Standard Time (EST)

Day 1 (Time)

Machine Learning for Engineers

8:45 AM

Log-in and System Check

9 AM

Course Overview

Install Python

Install Packages

9:30 AM

1️ Classification Overview

10:30 PM

Break

10:45 PM

2️ Classification Case Study

12 PM

Lunch Break

12:45 PM

3️ Regression Overview

2 PM

Break

2:15 PM

4️ Regression Case Study

3:30 PM

Knowledge Assessment and Review

4 PM

Conclude Day 1

Day 2

Data Science with TCLab

9 AM

TCLab Project / Introduction / Help / AIChE Academy Course

9:30 AM

1️ TCLab Overview

10:00 AM

2️ Import Data

10:30 AM

3️ Statistics

11 AM

4️ Visualize

11:30 AM

5️ Prepare Data

12 PM

Lunch Break

12:45 PM

6️ Regression

1:45 PM

7️ Features

2:15 PM

Break

2:30 PM

8️ Classification

3:30 PM

Knowledge Assessment and Review

4 PM

Conclude Day 2

Day 3

Data-Engineering with Python

9 AM

1️ k-Nearest Neighbors

10:30 AM

Break

10:45 AM

2️ Logistic Regression

12 PM

Lunch Break

12:45 PM

3️ Additive Manufacturing Case Study

2 PM

Break

2:15 PM

4️ Defect Detection Case Study

3:30 PM

Knowledge Assessment and Review

4 PM

Conclude Day 3

Day 4

Self-Guided Project

9 AM

Individual / Group Project Introduction

12 PM

Lunch Break

2:15 PM

Break

2:30 PM

Presentations

3:30 PM

Summarize Course and Certificates

4 PM

Conclude Day 4

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  • Course ID:
    CH270
  • Source:
    AIChE
  • Language:
    English
  • Skill Level:
    Intermediate
  • Duration:
    4 days
  • CEUs:
    2.40
  • PDHs:
    24.00
  • Accrediting Agencies:
    Florida
    New Jersey
    New York
    RCEP